The goals for this K01 proposal principally derive from a desire to better articulate and understand the processes and mechanisms associated with the development of alcohol use, abuse, and dependence. Integrative Data Analysis (IDA) has been identified as a useful framework for analyzing pooled data from extant longitudinal studies, and proffers a number of advantages over the discrete analysis of independent datasets. Specifically, IDA has the potential to (i) broaden the psychometric assessment of target constructs, (ii) extend the period of development under investigation, and (iii) provide direct tests of replication, all while increasing (iv) frequencies of low base-rate, high-risk behavior/trajectories, (v) representation of typically underrepresented subgroups, and (vi) statistical power. Fully capitalizing on this potential is, however, predicated on an ability to develop commensurate measures across independent studies, units of time, and psychometric instruments. With abundant evidence prospectively linking early externalizing behavior with drinking problems in adulthood, the focus of this project is on the phenotypic refinement of general externalizing problems within and across the following three studies: the Child Development Project, the Fast Track Project, and the Mobile Youth Survey. In doing so, various statistical methods for addressing measurement non-invariance will be applied and compared, including longitudinal extensions of item-response theory and, if possible, moderated non-linear factor analysis. The resultant reductions in measurement error are expected to yield more reliable and valid characterizations (both quantitative and qualitative) of the development of externalizing behavior, and to improve on our ability to identify critical early environmental and genetic antecedents of high-risk trajectories, as well as their alcohol-related sequelae in adulthood. As such, in the latter stages of the award it will be possible to directly assess the extent to which IDA procedures replicate, extend, and/or improve upon traditional single-study longitudinal analyses, and whether potential sources of between-study heterogeneity further inform on findings derived from data for which measurement non- invariance has been addressed across pooled samples.